Cost optimization in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Cost optimization

This pipeline shows how a machine learning model learns to predict the best way to reduce costs in a business. It starts with data about expenses, processes it, trains a model to find patterns, and then predicts cost-saving actions.

Data Flow - 7 Stages

1Raw Data Input

1000 rows x 10 columns→Collect business expense data including categories, amounts, and dates→1000 rows x 10 columns

Row example: { 'category': 'office supplies', 'amount': 200, 'date': '2023-05-01', ... }

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2Data Cleaning

1000 rows x 10 columns→Remove missing values and correct errors→980 rows x 10 columns

Removed 20 rows with missing 'amount' values

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3Feature Engineering

980 rows x 10 columns→Create new features like monthly spend, category frequency→980 rows x 15 columns

Added 'monthly_spend' and 'category_count' columns

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4Train/Test Split

980 rows x 15 columns→Split data into training (80%) and testing (20%) sets→Train: 784 rows x 15 columns, Test: 196 rows x 15 columns

Training set has 784 rows, testing set has 196 rows

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5Model Training

784 rows x 15 columns→Train a regression model to predict cost savings→Trained model

Model learns to predict potential cost reduction amount

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6Model Evaluation

196 rows x 15 columns→Evaluate model performance on test data→Performance metrics (loss, R2 score)

Test loss: 0.15, R2 score: 0.85

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7Prediction

New data sample with 15 features→Predict cost saving opportunities→Predicted cost saving value

Predicted saving: $500

Training Trace - Epoch by Epoch

Loss
0.9 |*       
0.7 | **     
0.5 |  ***   
0.3 |    ****
0.1 |      ***
     --------
     Epochs
1  2  3  4  5

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	N/A	Initial high loss as model starts learning
2	0.60	N/A	Loss decreases significantly, model improving
3	0.40	N/A	Loss continues to drop, learning stable
4	0.25	N/A	Model converging, loss reducing steadily
5	0.15	N/A	Low loss achieved, model ready for evaluation

Prediction Trace - 2 Layers

Layer 1: Input Features

Layer 2: Regression Model Prediction

Model Quiz - 3 Questions

What happens to the data shape after feature engineering?

A Number of columns decreases

B Number of rows decreases

C Number of columns increases

D Number of rows increases

Key Insight

This visualization shows how a model learns from expense data to predict cost savings. The steady decrease in loss means the model is improving its predictions, helping businesses find ways to reduce costs effectively.